{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data visualization example\n",
    "\n",
    "In a [recent blog post](https://www.allendowney.com/blog/2019/01/30/data-visualization-for-academics/), I showed figures from [a recent paper](https://osf.io/preprints/socarxiv/j2tw9/) and invited readers to redesign them to communicate their message more effectively.\n",
    "\n",
    "This notebook shows one way we might redesign the figures.  At the same time, it demonstrates a simple use of a Pandas MultiIndex."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The study reports the distribution of student evaluation scores for instructors under eight conditions.  At the top level, they report scores from evaluations with a 10-point of 6-points scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale = ['10-point', '6-point']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the next level, they distinguish fields of study as \"least\" or \"most\" male-dominated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "area = ['LeastMaleDominated', 'MostMaleDominated']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And they distinguish between male and female instructors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "instructor = ['Male', 'Female']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can assemble those levels into a MultiIndex like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex(levels=[['10-point', '6-point'], ['LeastMaleDominated', 'MostMaleDominated'], ['Female', 'Male']],\n",
       "           labels=[[0, 0, 0, 0, 1, 1, 1, 1], [0, 0, 1, 1, 0, 0, 1, 1], [1, 0, 1, 0, 1, 0, 1, 0]],\n",
       "           names=['Scale', 'Area', 'Instructor'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.MultiIndex.from_product([scale, area, instructor], \n",
    "                            names=['Scale', 'Area', 'Instructor'])\n",
    "index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each of these eight conditions, the original paper reports the entire distribution of student evaluation scores.  To make a simpler and clearer visualization of the results, I am going to present a summary of these distributions.\n",
    "\n",
    "I could take the mean of each distribution, and that would show the effect.  But to make it even clearer, I will use the fraction of \"top\" scores, meaning a 9 or 10 on the 10-point scale and a 6 on the 6-point scale. \n",
    "\n",
    "Now, to get the data, I used the figures from the paper and estimated numbers by eye.  **So these numbers are only approximate!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>TopScore%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scale</th>\n",
       "      <th>Area</th>\n",
       "      <th>Instructor</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">10-point</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">LeastMaleDominated</th>\n",
       "      <th>Male</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MostMaleDominated</th>\n",
       "      <th>Male</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">6-point</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">LeastMaleDominated</th>\n",
       "      <th>Male</th>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MostMaleDominated</th>\n",
       "      <th>Male</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        TopScore%\n",
       "Scale    Area               Instructor           \n",
       "10-point LeastMaleDominated Male               60\n",
       "                            Female             60\n",
       "         MostMaleDominated  Male               54\n",
       "                            Female             38\n",
       "6-point  LeastMaleDominated Male               43\n",
       "                            Female             42\n",
       "         MostMaleDominated  Male               41\n",
       "                            Female             41"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [60, 60, 54, 38, 43, 42, 41, 41]\n",
    "df = pd.DataFrame(data, columns=['TopScore%'], index=index)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To extract the subset of the data on a 10-point scale, we can use `loc` in the usual way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>TopScore%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Area</th>\n",
       "      <th>Instructor</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">LeastMaleDominated</th>\n",
       "      <th>Male</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MostMaleDominated</th>\n",
       "      <th>Male</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               TopScore%\n",
       "Area               Instructor           \n",
       "LeastMaleDominated Male               60\n",
       "                   Female             60\n",
       "MostMaleDominated  Male               54\n",
       "                   Female             38"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['10-point']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To extract subsets at other levels, we can use `xs`.  This example takes a cross-section of the second level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>TopScore%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scale</th>\n",
       "      <th>Instructor</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">10-point</th>\n",
       "      <th>Male</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">6-point</th>\n",
       "      <th>Male</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     TopScore%\n",
       "Scale    Instructor           \n",
       "10-point Male               54\n",
       "         Female             38\n",
       "6-point  Male               41\n",
       "         Female             41"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.xs('MostMaleDominated', level='Area')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example takes a cross-section of the third level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>TopScore%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scale</th>\n",
       "      <th>Area</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">10-point</th>\n",
       "      <th>LeastMaleDominated</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MostMaleDominated</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">6-point</th>\n",
       "      <th>LeastMaleDominated</th>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MostMaleDominated</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             TopScore%\n",
       "Scale    Area                         \n",
       "10-point LeastMaleDominated         60\n",
       "         MostMaleDominated          54\n",
       "6-point  LeastMaleDominated         43\n",
       "         MostMaleDominated          41"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.xs('Male', level='Instructor')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, now to think about presenting the data.  At the top level, the 10-point scale and the 6-point scale are different enough that I want to put them on different axes.  So I'll start by splitting the data at the top level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>TopScore%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Area</th>\n",
       "      <th>Instructor</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">LeastMaleDominated</th>\n",
       "      <th>Male</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MostMaleDominated</th>\n",
       "      <th>Male</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               TopScore%\n",
       "Area               Instructor           \n",
       "LeastMaleDominated Male               60\n",
       "                   Female             60\n",
       "MostMaleDominated  Male               54\n",
       "                   Female             38"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ten = df.loc['10-point']\n",
    "ten"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the primary thing I want the reader to see is a discrepancy in percentages.  For comparison of two or more values, a bar plot is often a good choice.\n",
    "\n",
    "As a starting place, I'll try the Pandas default for showing a bar plot of this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ten.unstack().plot(kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As defaults go, that's not bad.  From this figure it is immediately clear that there is a substantial difference in scores between male and female instructors in male-dominated areas, and no difference in other areas.\n",
    "\n",
    "The following function cleans up some of the details in the presentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_bar_plot(df):\n",
    "    # make the plot (and set the rotation of the x-axis)\n",
    "    df.unstack().plot(kind='bar', rot=0, alpha=0.7);\n",
    "    \n",
    "    # clean up the legend\n",
    "    plt.gca().legend(['Female', 'Male'])\n",
    "    \n",
    "    # label the y axis\n",
    "    plt.ylabel('Fraction of instructors getting top scores')\n",
    "    \n",
    "    # set limits on the 7-axis (in part to make room for the legend)\n",
    "    plt.ylim([0, 75])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the results for the 10-point scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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f2Ac1NlAzM6uMPL2Pdq1GIGZm1jJy3T4qaRjZMwEAtRHR8mOrmZlZs8hz++jFZPf/P5dep6cyMzNrA/JcERwODEoD1CBpHPAU7hrCzKxNyNvFRPHtoptWIhAzM2sZea4I/hd4StJksjGL9wPOr2hUZmZWNXnuGhovqRYYQpYIzk0Pi5mZWRuQp7F4UkTMj4gJEXFnRLwhaVI1gjMzs8qr94pAUhdgQ2DzNHiM0qJNgE9XITYzM6uCclVD/w2cQfZP/wk+SQSLgd9XOC4zM6uSehNBRFwGXCbpOxExuooxmZlZFeVpLHYSMLPquP7Ylo4gnzY2cFKlxyw2M7NWzonAzKyda7BqSNKuJYoXAa9ExIrmD8nMzKopz5PFfyAbYnIG2Z1DO6XpHpK+EREPVDA+MzOrsDxVQ3OBwWkg+d2AwcAzwMHALyoYm5mZVUGeRLB9RDxbmImI58gSw5zKhWVmZtWSp2polqTLgRvS/LHAC5LWB5ZXLDIzM6uKPFcEJwEvkj1lfCbZIPQnkSWBAysVmJmZVUeeB8o+AH6VXnW9V25bSXOBJcBKYEVE1EjqDtwI9CVrfzgmIt5pVNRmZtZs8vQ+uo+kiZJekDSn8GrEMQ6MiEERUZPmzwMmRUR/YBIe6czMrEXlaSMYS1Yl9ATZmf3aOgI4IE2PA2qBc5thv2Zm1gR5EsGiiLi3ifsP4AFJAVwZEWOALSJiPkBEzJf0qVIbSjoFOAWgT58+TTy8mZk1JE8imCzpl8BtwIeFwoh4Mse2+0TE6+mf/URJz+cNLCWNMQA1NTWRdzszM2ucPIlgj/SzpqgsgKENbRgRr6efb0m6HdgdeFNSr3Q10At4q5Exm5lZM8pz11CTbhGVtBHQISKWpOlDgIuACcBw4OL0886m7N/MzJpHuaEqT4yIayWdVWp5RFzawL63AG6XVDjO9RFxn6QpwE2SRgCvAkc3LXQzM2sO5a4INko/u5ZY1mCdfeqCYpcS5QuBg3JFZ2ZmFVduqMor0+TfIuKR4mWS9qloVGZmVjV5upgoNVSlh680M2sjyrUR7AXsDfSs006wCdCx0oGZmVl1lGsjWA/YOK1T3E6wGDiqkkGZmVn1lGsjeAh4SNIHEbHaADSSjgZmVzo4MzOrvDxtBMeVKPt+cwdiZmYto1wbwWHA4cCWkn5btGgTwIPWm5m1EeXaCF4HpgLDyHoeLVhC1hupmZm1AeXaCKYD0yVdn9brExGzqhaZmZlVRZ42gs8B04D7ACQNkjSholGZmVnV5EkEo8h6DX0XICKmkQ0zaWZmbUCeRLAiIhZVPBIzM2sRecYjeEbSV4COkvoDpwGPVjYsMzOrljxXBN8BdiQbnWw82ZPFZ1QyKDMzq548A9O8D/wgvczMrI1pMBFIuos1xx9YRPaMwZURsawSgZmZWXXkqRqaA7wHXJVei4E3ge3SvJmZrcPyNBYPjoj9iubvkvRwROwn6dlKBWZmZtWR54qgp6Q+hZk0vXma/agiUZmZWdXkuSL4LvB3SS8BAvoB35K0ETCuksGZmVnl5blr6K/p+YHtyRLB80UNxL9paHtJHckall+LiM9L6gfcAHQHngS+GhG+sjAzayF5qoaIiA8jYnpETGvCXUKnAzOL5n8O/Doi+gPvACMauT8zM2tGuRJBU0nqDfwX8Mc0L2AocEtaZRzwxUrGYGZm5VU0EZBVHX0P+DjN9wDejYjCwDbzgC1LbSjpFElTJU1dsGBBhcM0M2u/GkwEkvZJDcNIOlHSpZK2zrHd54G3IqJ4UBuVWLXuw2pZYcSYiKiJiJqePXs2dDgzM2uiPFcElwPvS9qF7Oz+FeAvObbbBxgmaS5Z4/BQsiuEbpIKjdS9yUZCMzOzFpK3G+oAjgAui4jLgK4NbRQR34+I3hHRFzgOeDAiTgAmA0el1YYDdzYpcjMzaxZ5EsESSd8HTgTuSbeDdl6LY54LnCXpRbI2g7FrsS8zM1tLeR4oOxb4CjAiIt5ITxb/sjEHiYhaoDZNzyEb8czMzFqBsokgnf1fGxEHF8oi4lXytRGYWSsx4uopLR1CLmPXa+kI2qeyVUMRsZKsoXjTKsVjZmZVlqdqaBnwtKSJwNJCYUScVrGozMysavIkgnvSy8zM2qA8nc6Nk7Qe2UA0ALMiYnllwzIzs2rJM1TlAWR9As0lezJ4K0nDI+LhyoZmZmbVkKdq6FfAIRExC0DSdsB4YLdKBmZmZtWR54GyzoUkABARL7B2D5SZmVkrkueKYKqkscA1af4E4Iky65uZ2TokTyL4JnAqcBpZG8HDwO8rGZSZmVVPnkTwjYi4FLi0UCDpdOCyikVlZmZVk6eNYHiJspOaOQ4zM2sh9V4RSDqerLO5fpImFC3qCiysdGBmZlYd5aqGHgXmA5uT3UJasASYUcmgzMyseupNBBHxCvCKpBOA1yNiGYCkDchGFptblQjNzKyi8rQR3MQng88DrARurkw4ZmZWbXkSQaeI+Kgwk6bda7iZWRuRJxEskDSsMCPpCODtyoVkZmbVlOs5AuA6Sb8je6DsX8D/rWhUZmZWNXm6oX4J2FPSxoAiYknlwzIzs2rJ0w31hXXmAYiIixrYrgtZdxTrp+PcEhE/ktQPuAHoDjwJfLW4DcLMzKorTxvB0qLXSuAwoG+O7T4EhkbELsAg4HOS9gR+Dvw6IvoD7wAjmhC3mZk1kzxVQ8UPkyHpEmBCPasXbxfAe2m2c3oFMJTsiWXIBrwZBVyeO2IzM2tWea4I6toQ2CbPipI6SpoGvAVMBF4C3o2IFWmVecCW9Wx7iqSpkqYuWLCgCWGamVkeedoIniY7kwfoCPQEyrYPFETESmCQpG7A7cAOpVarZ9sxwBiAmpqakuuYmdnay3P76OeLplcAbxad0ecSEe9KqgX2BLpJ6pT20Rt4vTH7MjOz5lW2akhSB+CeiHglvV7LmwQk9UxXAoX+iQ4GZgKTgaPSasOBO5scvZmZrbWyiSAiPgamS+rThH33AiZLmgFMASZGxN3AucBZkl4EegBjm7BvMzNrJnmqhnoBz0p6nOwWUgAiYlj9m0BEzAAGlyifA+zeyDjNzKxC8iSC/6l4FGZm1mLyJILDI+Lc4gJJPwceqkxIZmZWTXmeI/hsibLDmjsQMzNrGeXGLP4m8C1g29TgW9AVeKTSgZmZWXWUqxq6HrgX+F/gvKLyJRHx74pGZWZmVVNv1VBELIqIucAPgTfSGMb9gBMLzweYmdm6L08bwa3ASkn/QXbPfz+yqwUzM2sD8iSCj9PTxF8GfhMRZ5I9W2BmZm1AnkSwXNLxZMNT3p3KOlcuJDMzq6Y8ieBrwF7ATyPi5TTC2LWVDcvMzKolz8A0zwGnFc2/DFxcyaDMzKx68oxHsA/ZKGJbp/VFNgBZrsFpzMysdcvTxcRY4EzgCbIxi83MrA3JkwgWRcS9FY/EzMxaRJ5EMFnSL4HbgA8LhRHxZMWiMjOzqsmTCPZIP2uKygIY2vzhmJlZteW5a+jAagRiZmYto1zvoydGxLWSziq1PCIurVxYZmZWLeWuCDZKP7tWIxAzM2sZ9SaCiLgy/fRQlWZmbVieLiaaRNJWkiZLminpWUmnp/LukiZKmp1+blapGMzMrGEVSwTACuC7EbEDsCdwqqTPkA1yMyki+gOTWH3QGzMzq7J6E0HRGfw+TdlxRMwvPGsQEUuAmcCWwBHAuLTaOOCLTdm/mZk1j3JXBF9LP0ev7UEk9QUGA/8EtoiI+ZAlC+BT9WxziqSpkqYuWLBgbUMwM7N6lLtraKakuUDPOoPXFzqdG5jnAJI2Jhvl7IyIWCwpV2ARMQYYA1BTUxO5NjIzs0Yrd9fQ8ZL+D3A/MKwpO5fUmSwJXBcRt6XiNyX1ioj5knoBbzVl32Zm1jzKNhZHxBsRsQswn+x5gq7A62kg+7KUnfqPBWbWefhsAjA8TQ8H7mxK4GZm1jzyjEewP/AXYC5ZtdBWkoZHxMMNbLoP8FXgaUnTUtn5ZIPa3CRpBPAqcHQTYzczs2aQp9O5S4FDImIWgKTtgPHAbuU2ioi/kyWOUg5qTJBmZlY5eZ4j6FxIAgAR8QIevN7MrM3Ic0UwVdJY4Jo0fwLZaGVmZtYG5EkE3wROJRvAXsDDwB8qGZSZmVVPnvEIPiRrJ3C302ZmbVAl+xoyM7N1gBOBmVk750RgZtbO5XmgbDvgHGDr4vUjwoPXm5m1AXnuGroZuAK4ClhZ2XDMzKza8iSCFRFxecUjMTOzFpGnjeAuSd+S1CsNM9ldUveKR2ZmZlWR54qg0FPoOUVlAWzT/OGYmVm15XmgrF81AjEzs5aR566hzmTdTOyXimqBKyNieQXjMjOzKslTNXQ5WW+jhf6FvprKRlYqKDMzq548iWBIGqWs4EFJ0ysVkJmZVVeeu4ZWStq2MCNpG/w8gZlZm5HniuAcYLKkOWTdUG8NfK2iUZmZWdXkuWtokqT+wACyRPB86prazMzagHoTgaShEfGgpC/XWbStJCLitgrHZmZmVVDuimB/4EHgCyWWBVA2EUj6E/B54K2I2CmVdQduBPoCc4FjIuKdRkdtZmbNpt5EEBE/SpMXRcTLxcsk5XnI7Grgd8BfisrOAyZFxMWSzkvz5zYqYjMza1Z57hq6tUTZLQ1tFBEPA/+uU3wEMC5NjwO+mOP4ZmZWQeXaCLYHdgQ2rdNOsAnQpYnH2yIi5gNExHxJnypz/FOAUwD69OnTxMOZmVlDyrURDCCr4+/G6u0ES4CvVzIogIgYA4wBqKmpiUofz8ysvSrXRnAncKekvSLisWY63puSeqWrgV7AW820XzMza6I8bQTfkNStMCNps3RHUFNM4JNurYcDdzZxP2Zm1kzyJIKBEfFuYSbd7jm4oY0kjQceAwZImidpBHAx8FlJs4HPpnkzM2tBebqY6CBps8L9/ulZgDxPJB9fz6KDGhGfmZlVWJ5E8CvgUUmFW0aPBn5auZDMzKya8pzZ/0XSE8CBZH0NfTkinqt4ZGZmVhV5rgiIiGclLSA9PyCpT0S8WtHIzMysKhpsLJY0LDXuvgw8RNZH0L0VjsvMzKokz11DPwb2BF5IA9kfBDxS0ajMzKxq8iSC5RGxkOzuoQ4RMRkYVOG4zMysSvK0EbwraWPgYeA6SW8BKyoblpmZVUueK4IjgPeBM4H7gJcoPUaBmZmtg8peEUjqCNwZEQcDH/NJF9JmZtZGlL0iiIiVwPuSNq1SPGZmVmV52giWAU9LmggsLRRGxGkVi8rMzKomTyK4J73MzKwNKjdCWZ+IeDUi3C5gZtaGlWsjuKMwIanUuMVmZtYGlEsEKpreptKBmJlZyyiXCKKeaTMza0PKNRbvImkx2ZXBBmmaNB8RsUnFozMzs4orN3h9x2oGYmZmLSNPFxNmZtaGORGYmbVzLZIIJH1O0ixJL0o6ryViMDOzTNUTQerI7vfAYcBngOMlfabacZiZWaYlrgh2B16MiDkR8RFwA1lX12Zm1gIUUd1HBCQdBXwuIkam+a8Ce0TEt+usdwpwSpodAMyqaqBt2+bA2y0dhFkJ/m42r60jomdDK+XpdK65qUTZGtkoIsYAYyofTvsjaWpE1LR0HGZ1+bvZMlqiamgesFXRfG/g9RaIw8zMaJlEMAXoL6mfpPWA44AJLRCHmZnRAlVDEbFC0reB+4GOwJ8i4tlqx9HOucrNWit/N1tA1RuLzcysdfGTxWZm7ZwTgZlZO+dEkIOk9yq0326SvlU031dSSPpxUdnmkpZL+l0D+zop5zoLJD0labak+yXtvfbvZNX+H12LbU+S9OlGbtNX0jNNPWZbkr431xTNd0q/67ubsK++kr5SNH9A2v+IorLBqezsBvY1Kuc6r0malr6XtzVXbwOSPi3plrXY/gxJGzZymwOa8rm3JCeCltUN+FadsjnA54vmjwaaszH9xogYHBH9gYuB2yTt0Bw7joi1SSonAY1KBLaapcBOkjZI858FXmvivvoCX6lT9jRwbNH8ccD0Ju6/lF9HxKD0vbwReFBSgw9CNSQiXo+Io9ZiF2cAjUoE6yIngiaS1FPSrZKmpNc+qXx3SY+ms+5HJQ1I5TtKejyd9cyQVPhHvG0q+2Xa9QfATEmFh2qOBW5GfbWBAAAH3UlEQVQqOu4XJP0z7f9vkrbIG1tdETGZ7C6NU9J2gyT9I8V3u6TNUnmtpF9LeljSTElD0lnbbEk/KTrue+nnAWmbWyQ9L+k6SUrLLkwxPSNpjDJHATXAdemz2EDSbpIekvREunLplbbfTdJ0SY8Bpzbpl9d23Qv8V5o+HhhfWCCpu6Q70u/2H5IGpvL902c+LX2nupJ9L/8zlZ2ZdvEq0EXSFul3+bl0vML+v55+r9PTd2+Nf56StpV0X/qd/j9J25d6ExFxI/AAKRlJOijF9rSkP0laP5XPlfQzSY9Jmipp1/RdeUnSN9I6q64alV113pZimC3pF0WxXZ728ayk/0llp5GdnEyWNDmVHZKO96SkmyVtnMo/l77rfwe+nP9X1kpEhF8NvID3SpRdD+ybpvsAM9P0JkCnNH0wcGuaHg2ckKbXAzYgO/N6pmiffYFngGHAJWQP200iO1v+XVpnMz6522sk8Ks0XbxOfbGtWqfomF8E7k3TM4D90/RFwG/SdC3w8zR9OtkDgL2A9ckeEOxR/DkBBwCLUvwdgMeK4uledOxrgC8UHaMmTXcGHgV6pvljyW4zrhvjL4s/v/b8At4DBgK3AF2Aaen3cHfR9+9HaXooMC1N3wXsk6Y3JrulfNV2Rb/Pu4HTgG8D+wB/BkYBZ6d1ehSt/xPgO2m6eJ1JQP80vQfwYN11ivZxBnB5ei//ArZL5X8BzkjTc4Fvpulfp+9GV6An8Fbx31TR938OsGna7yvAVsXfS7Jb2muBgUXH2DxNbw48DGyU5s8FLiyKsT9Zzwk3FX9+68KrJbqYaCsOBj6TTnQBNklnU5sC49IZf5D9U4Psn+EPJPUGbouI2UXb1nUf8GPgTbLL5GK9gRvTGfJ6wMuNiK2Uwpn6pkC3iHgolY8Dbi5ar/DQ39PAsxExP203h+xJ8YV19vt4RMxL60wj+4P8O3CgpO+RXW53J6v2uqvOtgOAnYCJ6T10BOaXiPEasl5sDYiIGZL6kl0N/LXO4n2BI9N6D0rqkT7PR4BLJV1H9r2cV+Z7eRPZ93F7squN4qrAndLVYTeyhHJ/8YbpzHlv4Oai/a9f5u0UVhoAvBwRL6T5cWRXgr9J88Xfy40jYgmwRNIySd1K7HdSRCxKMT0HbE32T/wYZf2bdSI7yfkMWWIptmcqfyS9h/XI/q63TzHOTvu9lk/6SVsnOBE0XQdgr4j4oLhQ0mhgckR8Kf1R1gJExPWS/kl26X6/pJFkZydriIiPJD0BfBfYEfhC0eLRwKURMUHSAWRnU3ljK3W4wcDMcm80+TD9/LhoujBf6ntUvM5KoJOkLsAfyM78/yVpFNnZVF0iSzZ71Ym/GyX6pbLVTCC7mjwA6FFUXrKPr4i4WNI9wOHAPyQdXN+OI+INScvJ2h9OZ/VEcDXwxYiYLumkdPxiHYB3I2JQzvcxGJhaT9zFmuN72Q84GxgSEe9Iupr6v5cTI+L41QqlQazj30u3ETTdA2SXycCqLwNkVwSFRrqTipZvA8yJiN+S/bEOBJaQXcqW8ivg3Iioe6ZdvP/hjYxtNZL2JztzuSqdJb0j6T/T4q8CD5Xabi0U/rjeTmeIxY14xZ/FLKCnpL1SnJ0l7RgR7wKLJO2b1juhmeNrC/4EXBQRT9cpf5j0eaUTiLcjYrGkbSPi6Yj4Odk/3u0p/728kOx7ubJOeVeyq7bOlPi9RMRi4GVJR6cYJGmXUgeQdCRwCNlVx/NAX0n/kRZX4nu5CVlj+yJlbW7FV5nFn8U/gH0KsUjaUNJ2KcZ+krZN662WKNYFviLIZ0NJ84rmLyWrL/29pBlkn+PDwDeAX5BVDZ0FPFi0zbHAiemM6g2yP9Z/S3okNWbdSzZgDwCRdbtR6m6hUWSX16+RfTH7lVinvtgAjk3/SDckq1Y6MiIKVwTDgStSQ98c4GsNfzT5RcS7kq4iu4yfS9bvVMHV6dgfAHuRJYnfpuqLTmRVAc+mmP4k6X3qVD8YpOq4y0osGgX8OX0n3ueTk4gzJB1Idnb8HNn38GNghaTpZL+Xp4r2X98twhcA/ySrd3+a0onkBOByST8kqzK9gU/uPDpT0onARmTtZEMjYgGApK+Rfec7kX1nrij/KTROuop5iuz7NYesuqxgDHCvpPkRcWC62hmv1GAN/DAiXkjVSvdIepusCnSn5oyx0tzFhJlZO+eqITOzds6JwMysnXMiMDNr55wIzMzaOScCM7N2zonArB6SvqSsh82SfeKYtRVOBGb1O57snvDj6i6Q1LH64ZhVhhOBWQnpyed9gBGkRKCsV9XJkq4ne2gKSSfqk15lrywkCJXozdKstXIiMCvti8B9qbOzf0vaNZXvDvwgIj6jbByHY8l67xxE9nRuoXuFH0REDVlXIvsrdfts1ho5EZiVdjxZFwikn4X+Yx6PiEKPrwcBuwFTUg+rBwHbpGXHSHqSrHuGHcl6rTRrldzXkFkdknqQ9dm/k6Qg6wY7yLp2Xlq8KjAuIr5fZ/u8vVmatQq+IjBb01HAXyJi64joGxFbkXXQt2+d9SYBR0n6FKwaBWxryvdmadbqOBGYrel44PY6ZbdSZxzfiHgO+CHwQOrVcyLQKyKmk1UJPUvWLfQjmLVi7n3UzKyd8xWBmVk750RgZtbOORGYmbVzTgRmZu2cE4GZWTvnRGBm1s45EZiZtXP/H4aDDz6AlWkMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_bar_plot(ten)\n",
    "plt.title('10-point scale');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here are the results for the six-point scale, which show clearly that the effect disappears when a 6-point scale is used (at least in this experiment)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "six = df.loc['6-point']\n",
    "make_bar_plot(six)\n",
    "plt.title('6-point scale');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Presenting two figures might be the best option, but in my challenge I asked for a single figure.\n",
    "\n",
    "Here's a version that uses Pandas defaults with minimal customization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/UVH/dsDsFo7ZzOqgrR75PoY0fDQWQNIjpCGbDSXNBU6JiAdIO+TbJf2ANLRzQwP1LQI+IWkK6QSC0hl1JwHXSFofeIGVj9s05l7gznzQ/gxga9KxoAZFxJ8beKtaHB2BW/MQoEhnK86XVLne8vpvr7LOGZKmkYbZXgAerVLmNUnDgNsklcayvgM8l1+PkPQusB7pMzksLzdV0nBgYi73q4iYlk/maCkjgHslTQamA8/kdc/LJ13MBv4vIr4haQfgMUkAC4ETcox35GX/RlkCzQl3G2ByC8ZrZnWi1HGpoWA6YL8jZcMxEXFzjcsOAL4WEdVOLW8ySQsjYpWz0lqSpFuBsyLitUYLWyFIOgLYJSK+21jZgb27xeQfHNAGUZnVUUFPkpA0JSJWe80m1NiDkvQ90vU6OwJ/IA0r/Yl0tlqj8q/w8ZI6lg+HFVlEnFDvGKzJOgE/qXcQZtYyaj0GdRSwH/CviPg8sDNpeKhmEXFjSyWn1u492dopIn4XEbWcMWpma4FaE9TifHrxEkkfIp3K27v1wjIzs/au1pMkJivdr+160rVDC1lxIN3MzKzF1ZSgIqJ0j7RrJN0PfKj8zgVmZmYtraYhPiUnSDo/37Jofun+a2ZmZq2h1mNQVwGfIt3WB9JFlb9slYjMzMyo/RjUrvnmqtMg3R1CLfSsJjMzs2pqTVDv53u2BYDS4y2WtVpUZq2tW+/CXsRoZkmtQ3y/IN1R+yOSLiZdpHtJq0VlZmbtXq1n8Y3I973bj3QfucMj4ulWjczMzNq1RhNUvgv3zIjYiXxjTzMzs9bW6BBfvoPEDElbtEE8ZmZmQO0nSWwGPClpIulRFwBExKGtEpWZmbV7tSaoC1s1CjMzswq1niSx0pNpJe1BepJtU55Ya2ZmVrOan6grqT8pKX0OeBG4q7WCMjMzW22CkrQdcAzpFkfzgDtIT+Hdpw1iMzOzdqyxHtQzwCPAf0TEXwAkndXqUZmZWbvX2GnmRwL/AsZLul5S6UJdMzOzVrXaBBURIyNiKNAHmACcBWwq6WpJB7ZBfGZm1k7VdC++iFgUESMi4hCgJzAdOK9VIzMzs3at1pvFLhcR/46Ia32ihJmZtaYmJygASU/nv9NbOiAzMzNownVQ5SJiB0mbALu1cDxmZmZAE3pQkraUtH9+3QV4LyJ+32qRmZlZu1ZTgpL0BeBO4No8qycwqrWCMjMzq7UH9WVgD+BNgIh4HvhIawVlZmZWa4J6NyLeK01I6gRE64RkZmZWe4J6SNK3gC6SDgB+B9zbemGZmVl7V2uCOhd4DZgF/DfwB+A7rRWUmZlZrc+DWgZcn//MzMxaXWOP25jFao41RUS/Fo/IzMyMxntQh7RJFGZmZhVWm6Ai4m9tFYiZmVm5Wi/U3U3SJEkLJb0naamkN1s7ODMza79qPYvvStJj358HugCnAle0VlBmZmY13yw2Iv4iqWNELAV+LenPrRiXWauaM28RpwyfVO8wbC12w7BB9Q7hA6/WBPW2pHWB6ZJ+DLwMbNB6YZmZWXtX6xDfibns6cAi4OPAka0VlJmZWa09qNdJj9d4B7hQUkdgvdYLy8zM2rtae1DjgPXLprsAY1s+HDMzs6TWBNU5IhaWJvLr9VdT3szMbI3UmqAWSdqlNCFpILC4dUIyMzOr/RjUmcDvJP2TdG++jwFDWy0qMzNr91bbg5I0SNJHI2IS0Ae4A1gC3A+82AbxmZlZO9XYEN+1QOlJup8CvgX8EngDuK4V4zIzs3ausSG+jhHx7/x6KHBdRNwF3CVpeuuGZmZm7VljPaiOkkpJbD/gj2Xv1XybJDMzs6ZqLEHdBjwk6R7SWXuPAEjaBljQnBVK6iLpoXyxL5K2kDRG0tOSnpLUqzn1VlnPHyR1baTMMEkfq6GuCySd3RJxVan7cEk7lk0Pl/S2pI3K5v1cUkjq3khdc2osMyv/PSXpB5Ja5KJrSV+U9F/NXLaXpOOasdxwSUfl17dL2rY56zez4lltgoqIi4GvA8OBPSOi9HTdDsAZzVznycDd+aazADcDl0XEDsBg4NVm1ruSiDg4IuY3UmwY6YzEejoc2LFi3l+AwwAkdQD2Af7RguvcJyL6ktq7Ny10PDEiromIm5u5eC+gyQmqwtXAN9ewDjMriEavg4qIxyNiZEQsKpv3XERMbeY6jwfuAcg9h04R8WCud2FEvF25gKQhkh6WNDL/6r8m77iRdGzuDcyWdGnZMnMkdc+/zJ+WdL2kJ3NvrUv+1T0QGCFpuqQuTd0QSSdImpiXv7asV3i1pMl5fReWlf9Rjn+mpMsl7Q4cClyW69g6F72NFafxDwEeJZ09WapnlKQpuf7TmhJbuXzB9ReBwyV1U3JZbstZkoaWtf9Dkn4r6bm8Hcfn+meV4i7vaUqaIOnSXOY5SXvl+b0kPSJpav7bPYfzI2CvHO9ZkjrmWCbl9vrvvLwkXZnb8ffAR8o26RFgf60YljaztVib/kdWuiN674iYk2dtB8yXdDewFen2SeeW9a7KDSb1NP5GOs39P5Ue+XEp8EnSmYVjJB0eEaMqlt0WODYiviDpt8CREXGrpNOBsyNicjO2ZQdSEtkjIt6XdBUp+d4MfDsi/p2TwjhJ/YC5wBFAn4gISV0jYr6k0cB9EXFnrhfSc7cOk/Rh0nO4bgU+W7b6k3P9XYBJku6KiHk1xraSiHhT0ou5jXoC/YGdge657odz0Z2BHYB/Ay8Av4qIwZK+SupNn1mlmTrlMgcD3wP2J/WQD4iId/Jw3G2kHwrnkj6LQ/I2nAYsiIhBeQjyUUljgAHA9kBfYFPgKeDGvC3LJP0lxzqlymd2GnAawBbd1+eGdS+vErJZjX5T9vq4O+oWxgdZW//S7A6UD7t1AvYi7XReIl1nNQy4ocqyEyPiBQBJtwF7Au8DEyLitTx/BLA3UJmgXoyI0lmHU0jDSWtqP1JinJSTShdWDE9+Lu8MOwGbkRLrU8A7wK/yL//7Gqn/buAYYFfgvyve+4qkI/Lrj5OSy7yy91cXWzXK/+4J3JZ/ILwi6SFgEPAmMCkiXgaQ9FdgTF5mFmkIsqFtgJXbfB3gSkn9gaWkHynVHAj0yz1dgI3zdu5dFuM/Jf2xYrlXScO2qySoiLiOPJw5sHe3qHzfzIqlrRPUYqBz2fRcYFpZ4hkF7CZpNukaLIDzSTvIyh1KsGLH2ph3y14vJe2w15SAmyLivJVmSlsBZwODIuINScNJ9zJcImkwKXkcQ3p0yb6rqf92YGpex7KcaJA0hNQT+VREvC1pAiu3aYOxVd2IdDJGL+A5Vt+e5W24rGx6GQ1/j0pllpaVOQt4hdTL6UBK2lVDA86IiAcq4j2YVb8L5Trj23CZfSDUei++FhERb5BOXS/tUCcBH5bUI0/vCzwVEU9ERP/8Nzq/N1jSVkrHnoYCfwKeAD6tdKypI2k47KEmhPQWUH623A/LeiaNGQccJekjedlukrYEPkR6ZtYCSZuSh+YkbQhsHBF/IA2H9a8WQ0lEvAR8G7iq4q2NgTdycuoD7NaE2FaSY7oKGJU/m4eBofn4Tw9Sb2Vije1Rq42BlyNiGek5Y6VjY5Xt8ADwP5LWybFuJ2mDHOMxOcbNWLX3th3wZAvHbGZ1UI+DyWNIQ0ljI2JpPqg+TqmLMAW4voHlHiMdSO9L2kmNzD2L84DxpF/cf4iIe5oQy3DgGkmLSXfK6AuMbqDsdyQtP84SET0lfYd03KsDabjxyxHxuKRppJ3kC6QTHCDtfO/JyVmkngSkntL1kr4ClIazSuu4llXdD3xR0kzgWeDxygIR8VS12EjH7wDG5/buAIwEvp/nj8ztMIPUS/lmRPwrJ8KWchXpQu+jSZ9b6eSbmcASSTNIn8vPST27qTnW10hnPI4k/ZCZRer1Lf9Bkn8QLC4NRZrZ2k0rzhxvoxVKA4CvRcSJTVhmCGUH0FuLpAci4qDWXIe1HklnAW9GRLVjmCsZ2LtbTP7BAW0QlbULPkmiSSRNiYiBjZVr0yE+gIiYRvoFv8ppz/Xm5LTWmw/cVO8gzKxl1OV6kYi4sYnlJwATWiUY+8CIiF/XOwYzazlt3oMyMzOrhROUmZkVkhOUmZkVkhOUmZkVkhOUmZkVkhOUmZkVkh9LYO1Tt96+uNKs4NyDMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQnKCMjOzQupU7wDM6mHOvEWcMnxSvcOwtcgNwwbVO4R2xz0oMzMrJCcoMzMrJCcoMzMrJCcoMzMrJCcoMzMrJJ/FZ2bWTO+//z5z587lnXfeqXcohdS5c2d69uzJOuus06zlnaDMzJpp7ty5bLTRRvTq1QtJ9Q6nUCKCefPmMXfuXLbaaqtm1eEhPjOzZnrnnXfYZJNNnJyqkMQmm2yyRr1LJygzszXg5NSwNW0bJygzMyskH4MyM2shLX37rFpur9SxY0f69u27fHrUqFH06tWrReMoGT58OJMnT+bKK69slfor1b0HJamLpIckdczTW0gaI+lpSU9J6tVC6/mDpK6NlBkm6WM11HWBpJC0Tdm8s/K8gc2IbaX1Spog6SWV9Y8ljZK0sIa6aimzVNJ0SU9KmiHpa5Ja5Lsg6SJJ+zdz2f6SDm7GchNK7S5prKQPN2f9ZmujLl26MH369OV/rZWc6qHuCQo4Gbg7Ipbm6ZuByyJiB2Aw8GpLrCQiDo6I+Y0UGwY0mqCyWcAxZdNHAU81I7SG1jsf2AMgJ9bNmll3NYsjon9EfAI4ADgY+F5LVBwR50fE2GYu3j/HsiZuAb60hnWYrdWWLl3KN77xDQYNGkS/fv249tprAZgwYQKf/vSn+dznPsd2223Hueeey4gRIxg8eDB9+/blr3/9KwD33nsvu+66KwMGDGD//ffnlVdeWWUdr732GkceeSSDBg1i0KBBPProoy2+HUVIUMcD9wBI2hHoFBEPAkTEwoh4u3IBSUMkPSxpZO5lXVPqAUg6VtIsSbMlXVq2zBxJ3SX1yr2z63MPYkzuxR0FDARG5N5Fl0biHgUcluvuDSwAXitb3ypxSOooaXieNyv3uhpa7+2sSID/CdxdVveGksZJmprrOaxagJK+IWmSpJmSLqxWJiJeBU4DTlfSWdKvc73TJO2T6xqWe3H3SnpR0um55zVN0uOSuuVyw/M2ldr8wrI4++T5gyX9OS/7Z0nbS1oXuAgYmtthqKQNJN2Yt2FaaTvz53V73q47gPLPajRwbCOfndkHxuLFi+nfvz/9+/fniCOOAOCGG25g4403ZtKkSUyaNInrr7+eF198EYAZM2bw85//nFmzZnHLLbfw3HPPMXHiRE499VSuuOIKAPbcc08ef/xxpk2bxjHHHMOPf/zjVdb71a9+lbPOOotJkyZx1113ceqpp7b4ttX1GFTeKfWOiDl51nbAfEl3A1sBY4Fzy3pX5QYDOwJ/A+4H/lPSn4FLgU8CbwBjJB0eEaMqlt0WODYiviDpt8CREXGrpNOBsyNicg3hvwn8XdJOpER1B/D5vF0fqxYH8Hdg84jYKZfrGhHzK9ebR/bGAdcrDX0eQ0oi383rfgc4IiLelNQdeFzS6IiIsrY9MG/nYEDAaEl7R8TDlRsSES/kBP8R4IQ8r29OKGMkbZeL7gQMADoDfwHOiYgBkn4G/Bfwv1Xa6fWI2EXSl4CzgVOBZ4C9I2JJHg68JCKOlHQ+MDAiTs/bcAnwx4g4OfciJ0oaC/w38HZE9JPUD5hati1vSFpP0iYRMa88EEmn5XZki+7rc8O6l1cJ16whd9Q7gKpKQ3zlxowZw8yZM7nzzjsBWLBgAc8//zzrrrsugwYNYrPN0oDM1ltvzYEHHghA3759GT9+PJCu7xo6dCgvv/wy7733XtXrmMaOHctTT60YNHrzzTd566232GijjVps2+rdg+pOGsoq6QTsRdqRDQJ6k4a/qpkYES/k5HV7TNeiAAARYUlEQVQbsGdeZkJEvBYRS4ARwN5Vln0xIkqf6BSgVzPjL/VyDgdGls1vKI4XgN6SrpD0GVKSa8hS4E/AUKBLWRKHlHAukTSTlMQ3BzatWP7A/DeNtAPvQ0pYDSkd79qTNExGRDxD+gFQSlDjI+KtiHiN1GO8N8+fRcNtWOr5lbfzxsDvJM0GfgZ8ooFlDwTOlTQdmEBKjFuQ2vLWHONMYGbFcq9SZag2Iq6LiIERMbDHRus1sEqztV9EcMUVVyw/LvXiiy8uT0Trrbfiu9+hQ4fl0x06dGDJkiUAnHHGGZx++unMmjWLa6+9tuq1TMuWLeOxxx5bvo5//OMfLZqcoP4JajFpp1MyF5iWE88S0jDaLpJ2zcM+0yUdmstGRV3Bip1sY94te72U5vck7wVOBF6KiPJkUzWOiHgD2Jm0s/0y8KtG6r8duAL4bcX844EewCcjoj/wCiu3YymGH+ZjTf0jYpuIuKHaSvIQ5VLSjn11bVjebsvKppfRcBuWypS38/dJyW4n4D+qxF6+DUeWbcMWEfF0fq/y8y/XmfTdMmuXDjroIK6++mref/99AJ577jkWLVpU8/ILFixg8803B+Cmm26qWubAAw9c6Wy+yl5cS6jrEF8ejukoqXNEvANMAj4sqUf+lb4vMDkiniAdQAfSMShgsKStSL/whwLXAU8AP8/DXm+QjkVc0YSQ3gKW/wSQ9ENST21ktcIRsVjSOcBzFW9VjSNPvxcRd0n6KzC82nrLPAL8kNRDLLcx8GpEvJ+PEW1ZZdkHgO9LGhERCyVtDryfjzktJ6kHcA1wZUSEpIdJCfCPeWhvC+BZYJdqbdBMGwP/yK+Hlc2vbIcHgDMknZFjGxAR04BSjOPzEGu/su0R8FFgTgvGa1aTojx199RTT2XOnDnssssuRAQ9evRg1KjKIx0Nu+CCCzj66KPZfPPN2W233ZYfvyr3i1/8gi9/+cv069ePJUuWsPfee3PNNde05GYU4jqoMaRhpbERsVTS2cC4vKOZAlzfwHKPAT8C+pJ2WCMjYpmk84DxpF/ff4iIe5oQy3DgGkmLgU/lukevboGIuL3KvJerxSFpZ+DXWnFK93kNrLdUTwDVDpSMAO6VNBmYTjqmUxnDGEk7AI/lY1oLSceXXgW65GGzdYAlpCG9n+ZFr8qxzMrvDYuId9WyV8v/GLhJ0teAP5bNH8+KIb0fknpa/wvMzN+HOcAhwNWkdpxJ2v6JZXV8Eng898DNPvAWLlz1ypIOHTpwySWXcMkll6w0f8iQIQwZMmT59IQJE6q+d9hhh3HYYaueezVs2DCGDRsGQPfu3bnjjtY9Lqey4+p1IWkA8LWIOLEJywwhnVRwSKsFltbzQEQc1JrrsJYl6efA6IgYt7pyA3t3i8k/OKCNorIPhONW3Rk//fTT7LDDDnUIZu1RrY0kTYmIRq8ZrfcxKPKQzfh8tlqhODmtlWY3lpzMbO1QhCE+IuLGJpafQDrRwGwlEdHQkLCZrWXq3oMyMzOrxgnKzMwKyQnKzMwKqRDHoMzMPhB+M7Rl66ty5mAlSZxwwgnccsstACxZsoTNNtuMXXfdlfvuu6/B5SZMmMDll1++2jL15h6UmdlabIMNNmD27NksXpxunvLggw8uvwvE2s4JysxsLffZz36W3//+9wDcdtttHHvsihv6T5w4kd13350BAwaw++678+yzz66y/KJFizj55JMZNGgQAwYM4J57mnJ/g9bjIT5rn7r1rmn4xGxtcMwxx3DRRRdxyCGHMHPmTE4++WQeeeQRAPr06cPDDz9Mp06dGDt2LN/61re46667Vlr+4osvZt999+XGG29k/vz5DB48mP33358NNtigHpuznBOUmdlarl+/fsyZM4fbbruNgw9e+ZmfCxYs4KSTTuL5559H0vIbyJYbM2YMo0eP5vLL053V3nnnHV566aW63yXDCcrM7APg0EMP5eyzz2bChAnMm7fiUWjf/e532WeffRg5ciRz5sxZ6V58JRHBXXfdxfbbb9+GETfOx6DMzD4ATj75ZM4//3z69u270vzyR2cMHz686rIHHXQQV1xxBaV7s06bNq1VY62Ve1BmZi2ljsc1e/bsyVe/+tVV5n/zm9/kpJNO4qc//Sn77rtv1WW/+93vcuaZZ9KvXz8igl69ehXi9PO6383crB4GDhwYkydPrncYtpbz3cwbt1bfzdzMzKwaJygzMyskJygzszXgwyQNW9O2cYIyM2umzp07M2/ePCepKiKCefPm0blz52bX4bP4zMyaqWfPnsydO5fXXnut3qEUUufOnenZs2ezl3eCMjNrpnXWWYetttqq3mF8YHmIz8zMCskJyszMCskJyszMCsl3krB2SdJbwKoPxml73YHX6x1EVpRYihIHFCeWosQBLRPLlhHRo7FCPknC2qtna7nVSmuTNLkIcUBxYilKHFCcWIoSB7RtLB7iMzOzQnKCMjOzQnKCsvbqunoHkBUlDihOLEWJA4oTS1HigDaMxSdJmJlZIbkHZWZmheQEZWZmheQEZe2KpM9IelbSXySd28brvlHSq5Jml83rJulBSc/nfz/cBnF8XNJ4SU9LelLSV+sYS2dJEyXNyLFcmOdvJemJHMsdktZt7VjyejtKmibpvjrHMUfSLEnTJU3O8+rx+XSVdKekZ/L35VNtGYcTlLUbkjoCvwQ+C+wIHCtpxzYMYTjwmYp55wLjImJbYFyebm1LgK9HxA7AbsCXczvUI5Z3gX0jYmegP/AZSbsBlwI/y7G8AZzSBrEAfBV4umy6XnEA7BMR/cuuOarH5/Nz4P6I6APsTGqbtosjIvznv3bxB3wKeKBs+jzgvDaOoRcwu2z6WWCz/Hoz0gXEbd0u9wAH1DsWYH1gKrAr6U4Fnap9bq24/p55h7svcB+gesSR1zUH6F4xr00/H+BDwIvkk+nqEYd7UNaebA78vWx6bp5XT5tGxMsA+d+PtOXKJfUCBgBP1CuWPKw2HXgVeBD4KzA/IpbkIm31Of0v8E1gWZ7epE5xAAQwRtIUSafleW39+fQGXgN+nYc9fyVpg7aMwwnK2hNVmddur7OQtCFwF3BmRLxZrzgiYmlE9Cf1YAYDO1Qr1poxSDoEeDUippTPbus4yuwREbuQhqO/LGnvNlpvuU7ALsDVETEAWETbDCsu5wRl7clc4ONl0z2Bf9YplpJXJG0GkP99tS1WKmkdUnIaERF31zOWkoiYD0wgHRfrKql0r9C2+Jz2AA6VNAe4nTTM9791iAOAiPhn/vdVYCQpcbf15zMXmBsRT+TpO0kJq83icIKy9mQSsG0+M2td4BhgdJ1jGg2clF+fRDoe1KokCbgBeDoiflrnWHpI6ppfdwH2Jx2IHw8c1VaxRMR5EdEzInqRvhd/jIjj2zoOAEkbSNqo9Bo4EJhNG38+EfEv4O+Sts+z9gOeass4fCcJa1ckHUz6ZdwRuDEiLm7Ddd8GDCE9ruAV4HvAKOC3wBbAS8DREfHvVo5jT+ARYBYrjrd8i3Qcqq1j6QfcRPo8OgC/jYiLJPUm9WS6AdOAEyLi3daMpSymIcDZEXFIPeLI6xyZJzsBv4mIiyVtQtt/Pv2BXwHrAi8Anyd/Tm0RhxOUmZkVkof4zMyskJygzMyskJygzMyskJygzMyskJygzMyskJygzBohaWm+q3Tpr1cL1Hl4+Y1qJV0kaf81rbeRdd4maaaksyrmf1HSfzWjvq6SvtSC8fWSdFxL1bcmJPXPlySUpodI2r1sullt1sC6zpS0fkvU9UHj08zNGiFpYURsuJr3O5Xdr63WOocD90XEnWsaX43r+yjwRERs2YJ19iJtw05V3usYEUubWN8Q8vVHTVimyW1fY73DgIERcXqevgBYGBGXt8K65uR1vd7Sda+p5nyOLao174brP/99EP5IO6bKecOA3wH3An8ENiTdCXsq6QLYw8rK/hcwE5gB3ALsDvybdKfo6cDWpEdxHJXL70e6KHQWcCOwXp4/B7iwbB19qsTVGfh1fn8a6ZEN5PUvzuvbq2KZC0iJAdKthi4FJgLPlcoCn8jzpue6tiVdwFqq8zLSRcjjgd+Q7jjQi5Xv3H42cEF+vQ0wNrfJ1NwGjwMLcn1nrWZbKtt+M+DhvNzsyu3LyxwMPAP8CfgFKbECbJDbeFJex2Gki1JfIt0odTpwDvAv4B+l9quxzdYnXdA6E7iDdCH0wIq4vgK8l7dxfJ53bJ6eDVxa/j0EfpLbaxzQo8p2Hp2XmwE8nOd1BC7Pdc4Ezqjhe3Z+bqtj8mdzPzCFdIF3n4bW1eL/9+r9n99//iv6H7A075imAyPzvGGke5V1y9OdgA/l192Bv5BuNvoJ0uMJuuf3SuWHkxNS+TRpp/x3YLs8/2bSzVxLO47SzuVLwK+qxPp14Nf5dZ+8o+1MRbKoWKZyZ/uT/PpgYGx+fQVwfH69LtClsk5SgloEbJWnK98vT1BPAEfk151JO/Mh5MTRyLZUtv3XgW/n1x2BjSq2r9SmpbhuY0WCuoR0dwiArqQEs0Fex5XV2qgJbXY2cG1+vRPpOVwrJaiyz7X0/fhY3s4epO/UH4HD83tR9hmcXx5fWV2zgM1L25P//R/SfRdLjw3pRuPfs2+W1TkO2Da/3pV0G6iq62rpPx+DMmvc4kgPjusfEUeUzX8wVtziRcAlkmaSegabA5uSbjp6Z+Thm2j8ljDbAy9GxHN5+iag/E7WpRu7TiElgEp7knppRMQzwN+A7RrfxJVUW8djwLcknQNsGRGLG1h2YkS8uLrK833mNo+IkTnOdyLi7SpFV7ct5W0/Cfh8HobrGxFvVdTTB3ihLK7byt47EDg3P+5jAmnHvcXq4m9AtTbbk9TLJCJmk3ovjRkETIiI1yINXY5gxee/jNQTA7g111/pUWC4pC+QkjWk+xtek+srfQcb+57dAcvveL878LvcRteSeqwNratFOUGZNd+istfHk371fjLSoyNeIe3sRNMe0VDtEQ/lSveBW0r6hd3U5Wuxyjoi4jfAoaQhvQck7dvAsuVtsoSV9zGdmxjj6sotX09EPEzauf4DuKXKyQurq0fAkWU/QLaIiKdXU74h1T6X5nwWTVlmle9VRHwR+A7prv3T8/37qn0HG1tPqX07kJ6J1b/sb4fVrKtFOUGZtYyNSc8Tel/SPkDpZIRxwOdK/3kldcvz3wI2qlLPM0AvSdvk6ROBh5oQx8OkZImk7Ui9gWebsiHV5BuYvhARvyDdzbofDW9DySvARyRtImk94BCASM+emivp8Fz3evkstsr6atoWSVuS2v560l3ad6ko8gzQu+zsy6Fl7z0AnJHv8I6kAXl+ZSyNbWs1fwI+l+vdEejbQLnyup8APi2pu6SOpONRpc+/AyvurH5crn8lkraOiCci4nzS04A/DowBvlh6bEj+Dtb0Pcuf1YuSjs7LStLOq1lXi3KCMmsZI4CBkiaTdqrPAETEk8DFwEOSZgClx1vcDnwjP6l061IlEfEO6Y7Rv5NUutv4NU2I4yqgY172DmBYtMzdt4cCs/MwTx/g5oiYBzwqabakyyoXiIj3gYtIO937yG2SnQh8JQ+J/hn4KGkIbImkGflU+Fq3ZQjpF/w04Ejg5xVxLCYds7tf0p9IiXNBfvv7wDrATEmz8zSkkz12zJcVDCWdkHFEnt6rxja7CuiRt/GcvH0LqpS7Dvg/SeMjPaH2vLz+GcDUiCg9zmIR8AlJU0hDxxdVqesySbPytjyc6/gV6bjWzPwdPK6J37PjgVPysk+STiRpaF0tyqeZm9kHnqQNI2Jh7in9Eng+In7WyuvsCKwTEe/kHyHjSCclvNfM+lZ7ucMHUbUxbDOzD5ovSDqJdAbiNNLB/ta2PjBe6enFAv6nucmpvXIPyszMCsnHoMzMrJCcoMzMrJCcoMzMrJCcoMzMrJCcoMzMrJD+P4FQkvZGNFbTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.unstack().plot(kind='barh', xlim=[0, 65], alpha=0.7);\n",
    "plt.gca().legend(['Female', 'Male'])\n",
    "plt.gca().invert_yaxis()\n",
    "plt.xlabel('Fraction of instructors getting top scores')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('gender_bias.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a little tuning, this could be a good choice.  It clearly shows that there is only a substantial difference in one of the four conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
